




"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import GLPNImageProcessor






class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a=7						,					_a=3						,					_a=18						,					_a=30						,					_a=400						,					_a=True						,					_a=32						,					_a=True						,					):





							"""simple docstring"""
							lowerCamelCase      =   parent
							lowerCamelCase      =   batch_size
							lowerCamelCase      =   num_channels
							lowerCamelCase      =   image_size
							lowerCamelCase      =   min_resolution
							lowerCamelCase      =   max_resolution
							lowerCamelCase      =   do_resize
							lowerCamelCase      =   size_divisor
							lowerCamelCase      =   do_rescale





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}






@require_torch
@require_vision
class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		GLPNImageProcessor if is_vision_available() else None





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   GLPNImageProcessingTester(self					)





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(_a						,					"""do_resize"""					)					)
							self.assertTrue(hasattr(_a						,					"""size_divisor"""					)					)
							self.assertTrue(hasattr(_a						,					"""resample"""					)					)
							self.assertTrue(hasattr(_a						,					"""do_rescale"""					)					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							pass





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase      =   self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							lowerCamelCase      =   prepare_image_inputs(self.image_processor_tester						,					equal_resolution=_a					)
							for image in image_inputs:
										self.assertIsInstance(_a						,					Image.Image					)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCamelCase      =   image_processing(image_inputs[0]						,					return_tensors="""pt"""					).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase      =   self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							lowerCamelCase      =   prepare_image_inputs(self.image_processor_tester						,					equal_resolution=_a						,					numpify=_a					)
							for image in image_inputs:
										self.assertIsInstance(_a						,					np.ndarray					)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCamelCase      =   image_processing(image_inputs[0]						,					return_tensors="""pt"""					).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase      =   self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							lowerCamelCase      =   prepare_image_inputs(self.image_processor_tester						,					equal_resolution=_a						,					torchify=_a					)
							for image in image_inputs:
										self.assertIsInstance(_a						,					torch.Tensor					)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCamelCase      =   image_processing(image_inputs[0]						,					return_tensors="""pt"""					).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)









"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     Union[str, Any]					=				{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"mctct"





				def __init__(			self						,					_a=8_065						,					_a=1_536						,					_a=36						,					_a=6_144						,					_a=4						,					_a=384						,					_a=920						,					_a=1e-5						,					_a=0.3						,					_a="relu"						,					_a=0.02						,					_a=0.3						,					_a=0.3						,					_a=1						,					_a=0						,					_a=2						,					_a=1						,					_a=0.3						,					_a=1						,					_a=(7,)						,					_a=(3,)						,					_a=80						,					_a=1						,					_a=None						,					_a="sum"						,					_a=False						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a						,					pad_token_id=_a						,					bos_token_id=_a						,					eos_token_id=_a					)
							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   attention_head_dim
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   layerdrop
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   pad_token_id
							lowerCamelCase      =   bos_token_id
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   conv_glu_dim
							lowerCamelCase      =   conv_dropout
							lowerCamelCase      =   num_conv_layers
							lowerCamelCase      =   input_feat_per_channel
							lowerCamelCase      =   input_channels
							lowerCamelCase      =   conv_channels
							lowerCamelCase      =   ctc_loss_reduction
							lowerCamelCase      =   ctc_zero_infinity

							# prevents config testing fail with exporting to json
							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   list(_a					)

							if len(self.conv_kernel					) != self.num_conv_layers:
										raise ValueError(
										    """Configuration for convolutional module is incorrect. """
										    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
										    f'but is `len(config.conv_kernel) = {len(self.conv_kernel					)}`, '
										    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)



